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https://github.com/mayankyadav23/air-bnb-data-analysis

Data analysis and insights from NYC Airbnb listings, focusing on key metrics such as host performance, neighborhood trends, pricing, and customer reviews. Comprehensive documentation of ETL processes and analytical methodologies is provided. Perfect for understanding Airbnb dynamics and decision-making in the NYC market.
https://github.com/mayankyadav23/air-bnb-data-analysis

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Data analysis and insights from NYC Airbnb listings, focusing on key metrics such as host performance, neighborhood trends, pricing, and customer reviews. Comprehensive documentation of ETL processes and analytical methodologies is provided. Perfect for understanding Airbnb dynamics and decision-making in the NYC market.

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README 

        
![image](https://github.com/user-attachments/assets/930bb8fb-d772-4dca-86dc-7bb03c7b8eae)



# Air-BNB Travel Data Analysis Project đź“Š



# ❓Exploring Airbnb’s Expansion in Personalized Travel Experiences: A Data-Driven Analysis of NYC Homestays



Airbnb is reshaping the travel landscape by offering more unique and tailored experiences for travelers. This analysis seeks to derive meaningful insights from historical booking data of homestay listings in New York City. To do so, we will employ the Extract-Transform-Load (ETL) process to analyze key aspects of the data, answering essential research questions related to:



Host Engagement & Performance

Neighborhood Popularity & Trends

Customer Pricing Strategies

Guest Reviews & Satisfaction

By exploring these factors, this report aims to uncover patterns and insights that can help enhance Airbnb's travel offerings and improve both host and guest experiences in NYC.



# đź›  Tools Used



1. Excel

2. Power BI

3. Power Query

4. PowerPoint

   

# 📉 Dashboard



![image](https://github.com/user-attachments/assets/81876088-7d46-41be-b1e3-0876c641fe35)



![image](https://github.com/user-attachments/assets/25b1e305-2ec4-48d9-b697-edc6273e13e3)



Watch the complete Dashboard video [Link](https://www.youtube.com/watch?v=KEbqDzawWGA)



# ✔️ Key Insights from NYC Airbnb Homestay Analysis



Our comprehensive analysis of Airbnb homestays in New York City reveals intriguing trends and preferences in customer behavior, host success, and pricing strategies. Below are the standout findings:



Manhattan: The Epicenter of Airbnb Activity

Manhattan dominates the Airbnb market with the highest number of bookings across all neighborhood groups. Notably, the top-earning hosts also have the majority of their bookings in this area, solidifying Manhattan as the most preferred destination for travelers.



Pricing Paradox: High Prices, Fewer Bookings

Interestingly, neighborhoods with the highest average pricing tend to experience fewer bookings. This suggests that while premium areas exist, they are less frequently chosen by guests.



Price vs. Reviews: Consistent Across the Board

When comparing average pricing with review scores, the pricing remains relatively consistent across various review ratings, indicating minimal correlation between price and customer review scores (out of 5).



Room Type Preferences

Travelers overwhelmingly prefer Entire Homes/Apartments and Private Rooms as their accommodation types. Despite being the most popular, these room types also boast lower average pricing compared to other options.



Manhattan’s Room Type Breakdown

Within Manhattan, 88% of the bookings are for Entire Homes/Apartments, reinforcing its appeal as a destination for guests seeking privacy and a complete space to themselves.



These insights offer a clear understanding of the current trends in the New York City Airbnb market, helping hosts and travelers make more informed decisions.



# đź—‚ Documentation



High Level Design Document [Link](https://github.com/user-attachments/files/17014813/HLD.BusinessAnalyst.iN.pdf)



Low Level Design Document [Link](https://github.com/user-attachments/files/17014819/LLD.BA.iN.pdf)



Architecture [Link](https://github.com/user-attachments/files/17014831/BA.Architecture.iN.pdf)



WireFrame  [Link](https://github.com/user-attachments/files/17014837/BA.Wireframe.iN.pdf)



Report [Link](https://github.com/user-attachments/files/17014898/Air-BNB.Data.Analysis.Report.pptx)



# đź“© Feedback



If you have any feedback, please reach out to me at [Linkedin](https://www.linkedin.com/in/mayankyadv?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app)